---
title: 'Software Engineer - AI Developer Productivity at Baseten'
canonical: 'https://feeny.ai/job/software-engineer-ai-developer-productivity-baseten-san-francisco-q3z85q15bdvt'
type: 'job'
last_seen: '2026-10-18'
---

# Software Engineer - AI Developer Productivity at Baseten

- **Company:** [Baseten](https://feeny.ai/companies/baseten)
- **Location:** San Francisco, CA
- **Compensation:** $165k–$330k
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-08-11
- **Last confirmed live:** 2026-10-18
- **Apply:** https://jobs.ashbyhq.com/baseten/e71e97a8-6367-4050-b6a9-42ce62ec78f1

## Job description

## ABOUT BASETEN

Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our [$1.5B Series F](https://www.baseten.co/blog/announcing-our-series-f/), led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.

## THE ROLE

Baseten's engineers want to work in an AI-first way. What's missing isn't enthusiasm — it's the platform underneath it. Today everyone assembles their own agent config, context files, and MCP servers, so the good patterns stay trapped in individual setups instead of becoming defaults everyone inherits. You'll build that platform: the agent configurations tuned to our monorepo, the context and tooling layer that makes agents competent in our codebase, the evals that tell us which approaches actually work, and the rollout mechanics that get a new engineer productive with agents in week one. You are not here to mandate how engineers use AI — you're here to make the good path the easy path. Success looks like teams adopting what you build because it beats what they'd cobble together themselves, not because a policy requires it. Platform engineer, not AI evangelist. Ship infrastructure, measure it, kill what doesn't work, let adoption be the referee. The playbook for AI-first SDLC doesn't exist at any company yet. You'll write ours.

## EXAMPLE INITIATIVES

Agent substrate — Repo-level context infrastructure that makes agents competent in our codebase ([CLAUDE.md/AGENTS.md](http://CLAUDE.md/AGENTS.md) conventions, architecture and domain context, and the tooling to keep it accurate as code moves). Internal MCP servers giving agents scoped access to CI, observability, incident tooling, deployment state, and docs. Shared skills, subagents, and hooks that encode Baseten workflows. Sandboxed environments where agents can build and test safely. The golden path — Project templates and onboarding that ship with AI tooling configured and working. Self-serve infrastructure so teams build their own agents without you as the bottleneck. Gateway, auth, cost controls, and audit logging for internal model access. The feedback loop — Eval harnesses that answer "is this config better than that one" against real Baseten tasks, not vibes. Instrumentation of AI tool usage and its downstream effects on cycle time, review latency, and change failure rate. Honest reporting, including on what you built that didn't pan out. Agents in the SDLC — Automation where agents earn their keep: PR review triage, test gap-filling, incident context assembly, migrations and refactors, codebase Q&A. Integrating agents into CI/CD with guardrails that make it trustworthy.

## RESPONSIBILITIES

- Own the internal AI developer platform end to end — architecture, build, rollout, operation, measurement.
- Evaluate and integrate third-party AI coding tools (Claude Code, Cursor, Codex, and whatever ships next quarter), and build the context layer that makes them work against our monorepo.
- Build frameworks that let other engineers create their own agents without deep LLM expertise.
- Establish the evaluation practice for AI-assisted development at Baseten, and use it to drive investment decisions.
- Drive adoption through developer experience — good defaults, clear docs, low friction — not mandate.
- Embed with teams to find where AI genuinely unblocks them, then generalize those wins into platform capabilities.
- Own the safety layer: permissions, secrets handling, audit trails, cost management.

## REQUIREMENTS

- Have 4+ years of relevant industry experience building and enabling AI native SDLC
- Strong proficiency in Python and/or Go, building tools other engineers depend on daily.
- Hands-on experience with LLMs and agent frameworks — tool calling, MCP, context management, orchestration, failure handling. You've shipped something agentic that real people used, not just prototyped.
- Deep personal fluency with AI coding tools and well-formed opinions about where they break down.
- Platform mindset: you build for adoption and self-service, treat internal engineers as customers, and would rather ship a good default than write a style guide.
- Developer tooling, CI/CD, and Kubernetes/Docker fundamentals.
- Comfort with ambiguity — this space invalidates its own best practices every few months.
- Excellent written communication. Much of your leverage is docs, templates, and examples that scale beyond conversations you're in.

## BENEFITS

- Competitive compensation, including meaningful equity
- (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents
- Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
- Paid parental leave
- Fertility and family-building stipend through Carrot
- (U.S. only) Company-facilitated 401(k)
- Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.

Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

## About Baseten

## Company Overview
- **One-liner**: Baseten is an AI infrastructure platform that provides high-performance inference infrastructure for deploying, optimizing, and managing AI models in production.
- **Entity Type**: Private (Series F)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2019
- **Founders**: Tuhin Srivastava (CEO), Amir Haghighat (CTO), Phil Howes (Co-Founder & Chief Scientist)

## Core Business
- **Primary industry/industries**: AI Infrastructure / Cloud Computing / MLOps
- **Target customers**: B2B, targeting AI-native companies, enterprises, and developers building and deploying large-scale AI applications. Notable customers include Abridge, Cursor, Lovable, Notion, and OpenEvidence.
- **Mission or purpose statement**: Provide the tooling, expertise, and hardware needed for teams to bring performant AI products to market as quickly and reliably as possible. The company's core belief is that "inference is everything" and that the future of AI will consist of millions of specialized models embedded into every product and workflow.

## Products & Services
- **Baseten Cloud**: A fully-managed, global deployment platform for AI models. Provides out-of-the-box performance optimizations, 99.99% uptime, and massive horizontal scale. Supports single-tenant and self-hosted (hybrid) deployments.
- **Baseten Inference Stack**: The proprietary, underlying technology that powers the platform. Includes custom model runtimes (LLMs, embeddings, voice AI, image generation), advanced caching, custom kernels, and cutting-edge performance research. Claims 2x higher throughput and 10% lower latency for embeddings (BEI).
- **Baseten Chains**: Enables granular hardware and autoscaling for compound AI systems, reportedly powering 6x better GPU usage and cutting latency in half.
- **Frontier Gateway (Pre-optimized Model APIs)**: Allows users to instantly test and deploy pre-optimized versions of the latest open-source, custom, and fine-tuned models (e.g., Qwen, DeepSeek, GLM, gpt-oss).
- **Forward Deployed Engineering (FDE)**: A service offering where Baseten engineers work side-by-side with key customers to build, optimize, and scale their models from prototype to production.
- **Training on Baseten**: Allows users to train models and deploy them in one click on the inference-optimized infrastructure.
- **Monetization API**: An inference API that allows model developers to monetize their models.

## Market Standing
- **Valuation/Market Cap**: Not privately disclosed, but the company has raised a total of **$585M** in funding.
- **Key Metric**: Annual revenue of **$7.0M** (as per LinkedIn). Total funding of **$585M**.
- **Notable Investors/Partners**: IVP, Spark Capital, Bond, CapitalG, South Park Commons, Greylock.
- **Growth Signals**:
    - Announced a **Series F** funding round (details not fully public in search results).
    - **Massive headcount growth**: 224.4% year-over-year, now employing 201 people.
    - **LinkedIn follower growth**: 199.9% year-over-year.
    - **Active job postings**: 112 open roles.
    - Acquired the company **Parsed**.
    - Backed by high-profile institutional investors including CapitalG (Alphabet's growth fund) and IVP.

## Competitive Advantages
- **High-Performance Inference Stack**: A proprietary, deeply optimized stack focusing on ultra-low latency and high throughput for the most demanding Gen AI workloads (voice AI, real-time streaming, complex compound AI systems).
- **Customer Obsession & Forward Deployed Engineering**: A hands-on, consulting-style approach with their FDE team, which builds deep, sticky relationships with high-value customers like Abridge and Notion.
- **Global & Hybrid Cloud Flexibility**: Offers both fully-managed cloud and self-hosted deployments within customer VPCs, providing scalability and security for enterprises with strict data governance requirements.
- **Founding Team's Deep Technical Credibility**: Built by former researchers and engineers who experienced the "deployment hell" of AI systems firsthand, giving them deep empathy and expertise in the problem they are solving.

## Strategic Focus
- **Dominating the "Inference-as-a-Service" Market**: The company's core thesis is that inference will become the largest market ever created, and they are building the infrastructure to power it.
- **Scaling for High-Volume, Mission-Critical Workloads**: Focus on reliability, performance, and cost-efficiency for customers who cannot afford downtime or latency (e.g., AI phone calls, real-time translation).
- **Deepening the Technical Stack**: Continuously investing in performance research (custom kernels, new decoding techniques) and expanding capabilities into new areas like voice AI and audio streaming.
- **Aggressive Hiring**: With 112 open positions and massive YoY growth, scaling the engineering, sales, and operations teams is a clear priority.

## Why Work Here
- **Hard Technical Problems**: Engineers work on the most challenging problems in modern infrastructure—model serving, low-level GPU optimization, networking, distributed systems, and observability. This is a high-agency, high-impact engineering environment.
- **High Growth Trajectory**: The company is experiencing explosive growth (224% headcount increase, active hiring). This offers significant career acceleration and ownership opportunities.
- **Strong Engineering Culture**: Founded by engineers, for engineers. The culture emphasizes "first-principles thinking across the entire stack" and a "customer-obsessed" mindset. The employer rating on compensation, culture, and work-life balance is rated highly (5.0).
- **Top-Tier Team & Investors**: The team has strong talent density with hires from Meta, Stripe, Google, NVIDIA, and Databricks. Being backed by top-tier VCs provides stability and a clear long-term vision.
- **Hybrid/In-Office**: Based in San Francisco with a strong in-person or hybrid culture common for fast-moving infrastructure startups.

## Sources
1. [Company Website](https://www.baseten.co/)
2. [Careers Page](https://www.baseten.co/resources/careers/)
3. [About Us (Team)](https://www.baseten.co/about-us/)
4. [LinkedIn Company Profile](https://www.linkedin.com/company/baseten)
5. [Ashby Careers Portal](https://jobs.ashbyhq.com/baseten)

## Other roles at Baseten

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- [Partner Enablement Lead](https://feeny.ai/job/partner-enablement-lead-baseten-san-francisco-hnn1ex9bg330) — San Francisco, CA
- [Engineering Manager Runtime - Storage](https://feeny.ai/job/engineering-manager-runtime-storage-baseten-san-francisco-tj58p3wmtdwz) — San Francisco, CA
- [Sales Manager - Enterprise (New York)](https://feeny.ai/job/sales-manager-enterprise-new-york-baseten-new-york-5tyvjykwwgvp) — New York, NY
- [GRC Manager](https://feeny.ai/job/grc-manager-baseten-san-francisco-h4rsmvjx5r79) — San Francisco, CA
- [Post-Training Applied Researcher](https://feeny.ai/job/post-training-applied-researcher-baseten-san-francisco-rkqr55d1q922) — San Francisco, CA
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